Question 1

An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

Options :

A : Train the model over multiple desktop computers

B : Train the model on GPUs

C : Use a contractor to do the training portion

D : Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer

Answer: B

Question 2

A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months. What's wrong with this timeframe?

Options :

A : It's underestimating the project timeline by 3 months

B : It fits into a waterfall timeframe, but not an agile project timeframe

C : It's not accounting for data preparation timelines

D : It's not accounting for potential project delays

Answer: D

Question 3

Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?

Options :

A : Biased data sets leading to algorithmic discrimination

B : Generative AI models hallucinating data results

C : Nothing is wrong, algorithmic decisions will never be 100%

D : Data privacy issues leading to data sharing concerns

Answer: A

Question 4

Your team is running a simulation-based optimization exercise to increase routing efficiency. Learning for this exercise is done through "trial and error". Which type of machine learning approach is being leveraged for this exercise?

Options :

A : Unsupervised Learning

B : Reinforcement Learning

C : Supervised Learning

D : All would work equally well

Answer: B

Question 5

You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What's your best course of action?

Options :

A : Keep going as planned and the problem will eventually correct itself

B : Try to get additional data - at least 5 training examples for each dimension in the representation

C : Try to get additional information from project lead to see how many examples per dimension are needed

D : Try to improve the quality of your data through more preparation

Answer: B

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